From e7db5e4dfbc6acd5e7b49e3e1b584e7cc095cc57 Mon Sep 17 00:00:00 2001 From: Siwat Sirichai Date: Mon, 14 Nov 2022 23:28:46 +0700 Subject: [PATCH] initial canny edge dect code --- kuukar/kuukar_motion.py | 2 +- lane_sample.jpeg | Bin 0 -> 158889 bytes requirements.txt | 4 +- siwatlanedetect.py | 120 ++++++++++++++++++++++++++++++++++++++++ 4 files changed, 124 insertions(+), 2 deletions(-) create mode 100644 lane_sample.jpeg create mode 100644 siwatlanedetect.py diff --git a/kuukar/kuukar_motion.py b/kuukar/kuukar_motion.py index 9bde2a7..fc29a1c 100644 --- a/kuukar/kuukar_motion.py +++ b/kuukar/kuukar_motion.py @@ -96,6 +96,7 @@ class motion: self.drive(0) def roam_start(self): + self.drive(25) self.roaming = True def roam_stop(self): @@ -117,7 +118,6 @@ class motion: turn_speed = 40 drive_speed = 25 - self.drive(drive_speed) f_dist = self.sensors.sonar_get_distance(1) if 0 < f_dist < sensitivity: # Close to collision, turn the vehicle print("collision") diff --git a/lane_sample.jpeg 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z85XGUY~60HxO~g~?$qVJP~n;~zc0?azW36hi5KmAG4hef{A%2`v3;&Y^2X3c1sZl8 znF+gD;}T_;o;K3McM>4rEO;tDTyCf>&a|Brgi~j(jVyjyG(-3(S`DJC!4lw*u zu=FK%SlC9)s>(l$?yxlvn%`(oo9_Pr66bXie9|U)>HO(rKO=v;Oh-?l{7uxfRM|Uy ry5Bo?P1o=>v;P1Koqy;MzxUmFoMgMp literal 0 HcmV?d00001 diff --git a/requirements.txt b/requirements.txt index e03d07c..27dbb27 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,6 @@ telemetrix-rpi-pico pyaudio flask -opencv-python \ No newline at end of file +opencv-python +numpy +matplotlib \ No newline at end of file diff --git a/siwatlanedetect.py b/siwatlanedetect.py new file mode 100644 index 0000000..ef71748 --- /dev/null +++ b/siwatlanedetect.py @@ -0,0 +1,120 @@ +#ROAD LANE DETECTION + +import cv2 +import matplotlib.pyplot as plt +import numpy as np + +camera = cv2.VideoCapture(1) + + +def grey(image): + #convert to grayscale + image = np.asarray(image) + return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + + #Apply Gaussian Blur --> Reduce noise and smoothen image +def gauss(image): + return cv2.GaussianBlur(image, (5, 5), 0) + + #outline the strongest gradients in the image --> this is where lines in the image are +def canny(image): + edges = cv2.Canny(image,50,150) + return edges + +def region(image): + height, width = image.shape + #isolate the gradients that correspond to the lane lines + triangle = np.array([ + [(100, height), (475, 325), (width, height)] + ]) + #create a black image with the same dimensions as original image + mask = np.zeros_like(image) + #create a mask (triangle that isolates the region of interest in our image) + mask = cv2.fillPoly(mask, triangle, 255) + mask = cv2.bitwise_and(image, mask) + return mask + +def display_lines(image, lines): + lines_image = np.zeros_like(image) + #make sure array isn't empty + if lines is not None: + for line in lines: + x1, y1, x2, y2 = line + #draw lines on a black image + cv2.line(lines_image, (x1, y1), (x2, y2), (255, 0, 0), 10) + return lines_image + +def average(image, lines): + left = [] + right = [] + + if lines is not None: + for line in lines: + print(line) + x1, y1, x2, y2 = line.reshape(4) + #fit line to points, return slope and y-int + parameters = np.polyfit((x1, x2), (y1, y2), 1) + print(parameters) + slope = parameters[0] + y_int = parameters[1] + #lines on the right have positive slope, and lines on the left have neg slope + if slope < 0: + print("ap left") + left.append((slope, y_int)) + else: + print("ap right") + right.append((slope, y_int)) + + #takes average among all the columns (column0: slope, column1: y_int) + right_avg = np.average(right, axis=0) + left_avg = np.average(left, axis=0) + #create lines based on averages calculates + left_line = make_points(image, left_avg) + right_line = make_points(image, right_avg) + print(left_line) + print(right_line) + print(np.array([left_line, right_line])) + return np.array([left_line, right_line]) + +def make_points(image, average): + print(average) + try: + slope, y_int = average + except TypeError: + return (np.array([0,0,0,0])) + y1 = image.shape[0] + #how long we want our lines to be --> 3/5 the size of the image + y2 = int(y1 * (3/5)) + #determine algebraically + x1 = int((y1 - y_int) // slope) + x2 = int((y2 - y_int) // slope) + return np.array([x1, y1, x2, y2]) + +while True: + '''##### DETECTING lane lines in image ######''' + + rv, image1 = camera.read() + plt.imshow(image1) + plt.show() + + + copy = np.copy(image1) + edges = cv2.Canny(copy,50,150) + isolated = region(edges) + print(edges) + plt.imshow(edges) + plt.imshow(isolated) + plt.show() + print("edge show") + cv2.waitKey(0) + + #DRAWING LINES: (order of params) --> region of interest, bin size (P, theta), min intersections needed, placeholder array, + lines = cv2.HoughLinesP(isolated, 2, np.pi/180, 100, np.array([]), minLineLength=40, maxLineGap=5) + averaged_lines = average(copy, lines) + black_lines = display_lines(copy, averaged_lines) + #taking wighted sum of original image and lane lines image + lanes = cv2.addWeighted(copy, 0.8, black_lines, 1, 1) + plt.imshow(lanes) + plt.show() + print("lane showed") + cv2.waitKey(0) \ No newline at end of file